IDEAS home Printed from https://ideas.repec.org/a/cje/issued/v48y2015i4p1321-1349.html
   My bibliography  Save this article

Identification of technology shocks using misspecified VARs

Author

Listed:
  • Ufuk Devrim Demirel

Abstract

Studies that evaluate the effects of technology shocks often employ structural VARs identified with long-run restrictions. In the presence of a mismatch between the lag structures of the true data-generating process and the adopted VAR, estimates based on long-run restrictions can be biased. This paper offers a method that can reduce this bias substantially. Using artificial data, I assess the performance of the proposed method and find that it can outperform a range of alternative procedures. Applying the procedure to the US data, I find that per-capita hours exhibit a positive hump-shaped response profile in response to a technology shock.

Suggested Citation

  • Ufuk Devrim Demirel, 2015. "Identification of technology shocks using misspecified VARs," Canadian Journal of Economics, Canadian Economics Association, vol. 48(4), pages 1321-1349, November.
    • Handle: RePEc:cje:issued:v:48:y:2015:i:4:p:1321-1349
    DOI: 10.1111/caje.12149
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/caje.12149
    Download Restriction: access restricted to subscribers
    File URL: https://libkey.io/10.1111/caje.12149?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jordi Galí & Pau Rabanal, 2005. "Technology Shocks and Aggregate Fluctuations: How Well Does the Real Business Cycle Model Fit Postwar US Data?," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 225-318, National Bureau of Economic Research, Inc.
    2. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    3. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 475-483, 04-05.
    4. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    5. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    6. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    7. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : II. New directions," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 309-341.
    8. John Galbraith & Aman Ullah & Victoria Zinde-Walsh, 2002. "Estimation Of The Vector Moving Average Model By Vector Autoregression," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 205-219.
    9. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    10. Peter N. Ireland, 2004. "Technology Shocks in the New Keynesian Model," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 923-936, November.
    11. Ramey, Valerie A & Francis, Neville, 2002. "Is The Technology-Driven Real Business Cycle Hypothesis Dead? Shocks and Aggregate Fluctuations Revisted," University of California at San Diego, Economics Working Paper Series qt6x80k3nx, Department of Economics, UC San Diego.
    12. Neville Francis & Valerie A. Ramey, 2009. "Measures of per Capita Hours and Their Implications for the Technology-Hours Debate," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1071-1097, September.
    13. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    14. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
    15. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    16. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
    17. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
    18. Harald Uhlig, 2004. "Do Technology Shocks Lead to a Fall in Total Hours Worked?," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 361-371, 04/05.
    19. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1379-1399, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    2. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
    3. Charles, Amélie & Darné, Olivier & Tripier, Fabien, 2015. "Are Unit Root Tests Useful In The Debate Over The (Non)Stationarity Of Hours Worked?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(1), pages 167-188, January.
    4. Laura Bisio & Andrea Faccini, 2010. "Does Cointegration Matter? An Analysis in a RBC Perspective," Working Papers in Public Economics 133, Department of Economics and Law, Sapienza University of Roma.
    5. Andrei Polbin & Sergey Drobyshevsky, 2014. "Developing a Dynamic Stochastic Model of General Equilibrium for the Russian Economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 166P, pages 156-156.
    6. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
    7. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    8. Rujin, Svetlana, 2024. "Labor market institutions and technology-induced labor adjustment along the extensive and intensive margins," Journal of Macroeconomics, Elsevier, vol. 79(C).
    9. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    10. Cantore, Cristiano & Ferroni, Filippo & León-Ledesma, Miguel A., 2017. "The dynamics of hours worked and technology," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 67-82.
    11. Jordi Gali & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBS Model Fit Postwar U.S. Data?," NBER Working Papers 10636, National Bureau of Economic Research, Inc.
    12. Sean Holly & Ivan Petrella, 2008. "Factor demand linkages and the business cycle: interpreting aggregate fluctuations as sectoral fluctuations," CDMA Conference Paper Series 0809, Centre for Dynamic Macroeconomic Analysis.
    13. Ambler, Steve & Guay, Alain & Phaneuf, Louis, 2012. "Endogenous business cycle propagation and the persistence problem: The role of labor-market frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 47-62.
    14. Malley, Jim & Woitek, Ulrich, 2010. "Technology shocks and aggregate fluctuations in an estimated hybrid RBC model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1214-1232, July.
    15. Elmar Mertens, 2005. "Puzzling Comovements between Output and Interest Rates? Multiple Shocks are the Answer," Working Papers 05.05, Swiss National Bank, Study Center Gerzensee.
    16. Nikolay Gospodinov & Alex Maynard & Elena Pesavento, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 455-467, October.
    17. Zhao, Ningru & Shi, Yukun & Sun, Yang & Miao, Jiaming, 2020. "Aggregate labor market fluctuations under news shocks," Economic Modelling, Elsevier, vol. 90(C), pages 397-405.
    18. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    19. Luca Guerrieri & Dale Henderson & Jinill Kim, 2020. "Interpreting shocks to the relative price of investment with a two‐sector model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 82-98, January.
    20. Gert Peersman & Roland Straub, 2009. "Technology Shocks And Robust Sign Restrictions In A Euro Area Svar," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 727-750, August.

    More about this item

    JEL classification:

    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cje:issued:v:48:y:2015:i:4:p:1321-1349. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Prof. Werner Antweiler (email available below). General contact details of provider: https://edirc.repec.org/data/ceaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.